Obesity and overweight are major risk factors for multiple diseases including cardiovascular disease, diabetes, cancer and stroke. Of particular importance is visceral, or abdominal, adipose tissue, which is a known predictor of metabolic health. Genetics play a large role in regulating visceral adiposity and obesity and understanding the underlying genetic mechanisms will help with prevention and treatment. Genome wide association studies (GWAS) and genetical genomics (integrating mRNA expression with genomic variation) in humans has led to identification of over 40 genes for anthropomorphic traits. Despite these successes, the vast majority of genetic variation remains unknown. With the small effect size of the remaining loci, it will be difficult to identify these loci in humans. Highly recombinant animal models such as heterogeneous stocks (HS) provide the ability to fine-map quantitative trait loci (QTL) to less than five Mb. My laboratory has used HS rats to fine-map over 60 QTL for visceral adiposity and related traits to an average of only 2.2 Mb. We have identified candidate genes within some of these loci, but additional methods are needed to identify and verify the remaining candidates. Using expression QTL (eQTL) mapping and HS founder sequence, we recently identified Tpcn2 as the likely causal gene underlying a 3.1 Mb locus for fasting glucose and insulin levels. Despite containing over 80 genes, the HS strategy enabled us to identify this gene within only a few years. In the current proposal, we hypothesize that HS rats harbor causal genes and variants for adiposity- related traits. We propose a combination of genetic and statistical tools to identify the causal genes and variants underlying many of these QTL. The major impact of this work will be to accelerate discovery of genes and networks involved in adiposity traits, thereby laying the foundation for improving health associated with visceral adiposity. In Aim 1 we will test the hypothesis that changes in RNA expression levels underlie QTL for adiposity traits. RNA-seq will be used to determine abundance levels of genes and transcripts in liver and adipose tissue of HS rats. Candidate genes and gene networks will be identified using eQTL and network analyses. We will identify cis-regulating eQTLs that fall within the same region as the adiposity QTL and use causal modeling and human and mouse GWAS to prioritize candidates. In Aim 2 we will test the hypothesis that adiposity QTL are regulated by a single sequence variant. Using the fully sequenced HS founders, we will impute all possible SNPs within each QTL and run a statistical merge analysis to identify potentially causal SNPs. Variants will be prioritized using conservation and protein modeling. We will validate five or six high priority candidate genes and/or variants using zinc-finger nuclease (ZFN) or clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) technology to manipulate these genes the rat. We expect that eQTL and network analysis, when combined with the statistical tools and rat knock-out/in technology, will allow us to validate causal genes underlying many of the already identified QTL for adiposity and related traits.